{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8bW5uwdFfj5K"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SONAL BANSAL\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "import random,math,copy,time\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import (RandomForestClassifier,GradientBoostingClassifier)\n",
    "from sklearn import tree\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing, svm\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S6-dFa8Jfj5Q"
   },
   "outputs": [],
   "source": [
    "m=20  # Problem dimension (number of decision variables\n",
    "Ap=0.1  # Awareness probability\n",
    "fl=2   # Flight length (fl)\n",
    "wf=1.2# wf is used to control the importance of classification accuracy and number of selected features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import up sound alert dependencies\n",
    "from IPython.display import Audio, display\n",
    "\n",
    "def allDone():\n",
    "    display(Audio(url='https://sound.peal.io/ps/audios/000/000/707/original/Good_Job!.wav', autoplay=True))\n",
    "## Insert whatever audio file you want above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VSQ4rCRjfj5V"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"AD-MCI.csv\")\n",
    "df = df.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yBZV56q2m6-s"
   },
   "outputs": [],
   "source": [
    "df=df.drop(columns=['Unnamed: 0'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "i5qnzPEforlt",
    "outputId": "e49cbf0d-6390-45dd-f8ee-52397198c00f"
   },
   "outputs": [
    {
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       "[5 rows x 67601 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PPwseQsIglXa"
   },
   "outputs": [],
   "source": [
    "val_Data = pd.read_csv(\"AD-MCI_val.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "wmWJFkWDgu_7",
    "outputId": "c147cdca-b994-4f9b-8b49-22c8ca7d71e1"
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    {
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      "[5 rows x 67601 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source src=\"https://sound.peal.io/ps/audios/000/000/707/original/Good_Job!.wav\" type=\"audio/wav\" />\n",
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       "<IPython.lib.display.Audio object>"
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     "metadata": {},
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   ],
   "source": [
    "val_Data = val_Data.dropna(how='all')\n",
    "val_Data=val_Data.drop(columns=['Unnamed: 0'])\n",
    "# df=df.reset_index().drop(columns='index')\n",
    "print(val_Data.head())\n",
    "allDone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_W4D-XnDfj5j"
   },
   "outputs": [],
   "source": [
    "features = [i for i in range(0,67600)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VvAFiZhvfj5n"
   },
   "outputs": [],
   "source": [
    "d=67600# Flock (population) size\n",
    "lt=67600\n",
    "y_train=df['label']\n",
    "x_train=df[df.columns[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K-e_XSa4pKr8"
   },
   "outputs": [],
   "source": [
    "y_test = val_Data['label']\n",
    "x_test = val_Data[val_Data.columns[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kgvbr_X7fj51"
   },
   "outputs": [],
   "source": [
    "# x=preprocessing.normalize(x)\n",
    "#x=pd.DataFrame(x, columns=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train.columns=features\n",
    "x_test.columns=features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67600"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imo=pd.read_csv('importance_feature_AD-MCI.csv').iloc[:,1]\n",
    "len(imo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JSz_SiuQfj6S"
   },
   "outputs": [],
   "source": [
    "def init():   #initialization function\n",
    "    df2=pd.DataFrame(np.ones((m,d),dtype='int'),columns=features)\n",
    "    return df2\n",
    "#df2=init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "colab_type": "code",
    "id": "EOfjU6K3fj6I",
    "outputId": "1ad247bc-7a6c-4522-f094-06b1cd2cf572"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n",
      "(67600,) <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "im=(rforest.feature_importances_)\n",
    "print(im)\n",
    "print(im.shape,type(im))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_df=pd.DataFrame(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_df.to_csv('importance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "colab_type": "code",
    "id": "TbeyhYYSfj6N",
    "outputId": "a1c0e68d-4931-4ab7-b484-404f847df13c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                        | 0/67600 [00:00<?, ?it/s]C:\\Users\\SONAL BANSAL\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 67600/67600 [20:17<00:00, 55.53it/s]\n"
     ]
    }
   ],
   "source": [
    "imo=pd.Series()\n",
    "for i in tqdm(range(d)):\n",
    "    imo= imo.set_value(features[i],im[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ylLmuEVjfj6X"
   },
   "outputs": [],
   "source": [
    "# this defines the fitness of each crow by calculating their accuracy and no features selected using the below formula\n",
    "def fitness(df2):      # fitness function whose 1 parameter takes the crows and their position as input\n",
    "    fnt=np.zeros((m,1))  # fnt is the fitness function\n",
    "    for k in tqdm(range(m)):    \n",
    "        global sel\n",
    "        sel=[]\n",
    "        q=0\n",
    "        imp=0\n",
    "        a=(df2.iloc[k,:])==0.0\n",
    "        if a.all()==True:\n",
    "            df2.iloc[k,:]=1.0\n",
    "        for p in list(features):\n",
    "            if df2.loc[k,p]==1.0:\n",
    "                sel.insert(q,p)\n",
    "                temp=imo[p]\n",
    "                imp=imp+temp\n",
    "            q=q+1\n",
    "        lf=len(sel)\n",
    "        divide=lf/lt\n",
    "        fnt[k]=imp+wf*(1-float(lf/lt))     # fitness formula\n",
    "    return fnt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3JX1sTSufj6c"
   },
   "outputs": [],
   "source": [
    "z=init()   #crow memory with initial position\n",
    "tmax=50   # Maximum number of iterations (itermax)\n",
    "xn=z.copy()  # the position of the crow\n",
    "mem=init()  # memory is initialized using the init function\n",
    "xnew=(xn).copy()   # the positions of the crow\n",
    "ft=fitness(xn)\n",
    "plotit=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "cRGlsQ57fj6j",
    "outputId": "ecb5e6e9-c09c-4581-d79f-55308fae3400"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|                                                                                           | 0/20 [00:00<?, ?it/s]\n",
      " 10%|████████▎                                                                          | 2/20 [00:00<00:00, 19.96it/s]\n",
      " 50%|█████████████████████████████████████████                                         | 10/20 [00:00<00:00, 45.37it/s]\n",
      " 90%|█████████████████████████████████████████████████████████████████████████▊        | 18/20 [00:00<00:00, 52.89it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 54.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|                                                                                           | 0/20 [00:00<?, ?it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 199.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running fitness\n"
     ]
    },
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     "text": [
      "fitness done\n"
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "exp\n",
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      "fitness done\n"
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "running fitness\n"
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      "exp\n",
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      "fitness done\n"
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      "exp\n",
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      "exp\n",
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     ]
    },
    {
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     "text": [
      "fitness done\n"
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    },
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      "complete19\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "complete20\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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     "text": [
      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "complete21\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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     "text": [
      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "complete22\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
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      "running fitness\n"
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "fitness done\n"
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    },
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      "complete23\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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    },
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     "text": [
      "running fitness\n"
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     ]
    },
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     "output_type": "stream",
     "text": [
      "fitness done\n"
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    },
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      "complete24\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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     "text": [
      "running fitness\n"
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     "text": [
      "fitness done\n"
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     "text": [
      "complete25\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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    },
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     "text": [
      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "complete26\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "running fitness\n"
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     ]
    },
    {
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     "text": [
      "fitness done\n"
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    },
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      "complete27\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "running fitness\n"
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     ]
    },
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     "output_type": "stream",
     "text": [
      "fitness done\n"
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    },
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      "complete28\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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     "text": [
      "running fitness\n"
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     ]
    },
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     "text": [
      "fitness done\n"
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      "complete29\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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     "text": [
      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "complete30\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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     ]
    },
    {
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     "text": [
      "fitness done\n"
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    },
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      "complete31\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "running fitness\n"
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    },
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     "text": [
      "fitness done\n"
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      "complete32\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
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      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "complete33\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "complete34\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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     "text": [
      "running fitness\n"
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      " 90%|█████████████████████████████████████████████████████████████████████████▊        | 18/20 [01:30<00:10,  5.04s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
    {
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      "\n",
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     ]
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     "text": [
      "complete35\n",
      "36\n"
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    },
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
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     ]
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    {
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     "text": [
      "running fitness\n"
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    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
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     "text": [
      "complete36\n",
      "37\n"
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    },
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     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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    },
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     ]
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    {
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     "text": [
      "running fitness\n"
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    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
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     "text": [
      "complete37\n",
      "38\n"
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     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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    },
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    {
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     "output_type": "stream",
     "text": [
      "running fitness\n"
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    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
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    },
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     "text": [
      "complete38\n",
      "39\n"
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     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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    },
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    {
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     "text": [
      "running fitness\n"
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    },
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     "text": [
      "fitness done\n"
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    },
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      "complete39\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "exp\n",
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      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "exp\n",
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      "running fitness\n"
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    },
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     "text": [
      "fitness done\n"
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    },
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      "complete43\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
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      "running fitness\n"
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     "text": [
      "fitness done\n"
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      "exp\n",
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      "transpose\n",
      "exp\n",
      "apply lambda\n",
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      "exp\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
    {
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      "\n",
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      "complete47\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
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    {
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      "running fitness\n"
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
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      "\n",
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      "complete48\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transpose\n",
      "exp\n",
      "apply lambda\n",
      "copied\n"
     ]
    },
    {
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    {
     "name": "stdout",
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     "text": [
      "running fitness\n"
     ]
    },
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitness done\n"
     ]
    },
    {
     "name": "stderr",
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      "\n",
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    {
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     "output_type": "stream",
     "text": [
      "complete49\n",
      "192.07184628564485\n"
     ]
    }
   ],
   "source": [
    "tottime=0\n",
    "tmax=50\n",
    "#s=pd.DataFrame(z)\n",
    "#print(\"got s\")    # it is a formula to convert positions of each crow into 0 and 1 \n",
    "for t in range(tmax):\n",
    "    print(t)# no of iterations\n",
    "    time1=time.time()\n",
    "   \n",
    "    xnew_list = []\n",
    "    plotit.append(ft.max())\n",
    "    num = random.randint(0,m-1)  \n",
    "   # Generation of random candidate crows for following (chasing)\n",
    "    for i in tqdm(range(m)):   \n",
    "       \n",
    "        r=random.random()\n",
    "        if r>=Ap:\n",
    "            \n",
    "            xnew_list.append((xn.loc[i,:]+fl*r*(mem.loc[num,:]-xn.loc[i,:])).tolist())# Generation of a new position for crow i (state 1)\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            xnew_list.append((xnew.loc[i,:].apply(lambda lam:random.random())).tolist())   # Generation of a new position for crow i (state 2)\n",
    "            \n",
    "            \n",
    "    xnew = pd.DataFrame(xnew_list)\n",
    "    del xnew_list\n",
    "    print('transpose')\n",
    "    xnew= 1/( 1 + np.expm1(( 10*(xnew-0.5))))    #  formula to convert positions of each crow into 0 and 1\n",
    "    print('exp')\n",
    "    xnew=xnew.apply(lambda l:l>=random.random()).astype(int)\n",
    "    print('apply lambda')\n",
    "    xn=xnew.copy()     \n",
    "    print('copied')\n",
    "    # it is the updated binary position of each crow\n",
    "    #feasibility test\n",
    "    # this is done so that to check if atleast one position of the crow has value equal to 1. if it is not true then every\n",
    "    # position value of the crow is made 1.\n",
    "    for i in tqdm(range(m)):\n",
    "        \n",
    "        if ((xn.loc[i,:])==0.0).all()==True:\n",
    "            xn.loc[i,:]=1.0\n",
    "    \n",
    "    print('running fitness')\n",
    "    ft_new=fitness(xn).reshape(ft.shape)# here the accuracy of each crows are calculated.\n",
    "\n",
    "    print('fitness done')\n",
    "    for i in tqdm(range(m)):\n",
    "         \n",
    "        if ft_new[i]>ft[i]:\n",
    "            ft[i]=ft_new[i]\n",
    "            for j in features:\n",
    "                mem.loc[i,j]= xn.loc[i,j]\n",
    "    \n",
    "    print('complete'+str(t))\n",
    "    time2=time.time()\n",
    "    tottime=tottime+(time2-time1)\n",
    "print(tottime/t+1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mem=pd.read_csv('memory.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mem = mem.dropna(how='all')\n",
    "mem=mem.drop(columns=['Unnamed: 0'])\n",
    "mem.columns=features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TESTING RESULTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without Grid-Search, Without Bio-inspired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1553560919.5732362\n",
      "1553561341.5669374\n",
      "1553561347.9873168\n",
      "1553561540.1038666\n",
      "1553591247.9768016\n",
      "0.7946666666666666 0.9173333333333333 0.8693333333333333 0.9386666666666666\n"
     ]
    }
   ],
   "source": [
    "print(time.time())\n",
    "\n",
    "neigh1 = KNeighborsClassifier()\n",
    "neigh1.fit(x_train, y_train)\n",
    "neigh1.predict(x_test)\n",
    "nacc=neigh1.score(x_test,y_test)\n",
    "\n",
    "print(nacc)\n",
    "\n",
    "#\n",
    "rforest1 = RandomForestClassifier()\n",
    "rforest1.fit(x_train, y_train)\n",
    "rforest1.predict(x_test)\n",
    "rfacc=rforest1.score(x_test,y_test)\n",
    "\n",
    "print(rfacc)\n",
    "\n",
    "#\n",
    "dtree1 = tree.DecisionTreeClassifier()\n",
    "dtree1.fit(x_train, y_train)\n",
    "dtree1.predict(x_test)\n",
    "dtacc=dtree1.score(x_test,y_test)\n",
    "\n",
    "print(dtacc)\n",
    "\n",
    "clf1 = svm.SVC()\n",
    "clf1.fit(x_train, y_train)\n",
    "clf1.predict(x_test)\n",
    "clfacc=clf1.score(x_test,y_test)\n",
    "\n",
    "print(clfacc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WITH GRID-SEARCH WITHOUT BIO-INSPIRED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 108 candidates, totalling 324 fits\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.2min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.0min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  15.4s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  13.0s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  14.4s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best, total=  56.2s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best, total=  54.7s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best, total=  52.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  16.0s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  13.4s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  13.0s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  15.2s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  12.5s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  13.6s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best, total=  57.8s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  15.5s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  13.0s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  13.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.0min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.0min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  15.8s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  14.1s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  14.4s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best, total=  53.1s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random, total=   8.8s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  11.4s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  13.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  53.2s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  51.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  47.3s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  13.8s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  12.3s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  12.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best, total=  54.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best, total=  51.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best, total=  57.4s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  14.7s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  11.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  11.8s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best, total=  52.4s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best, total=  53.5s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  13.9s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  12.0s\n",
      "[CV] criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  13.6s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  19.0s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  18.9s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  19.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  17.7s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  20.0s\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  19.0s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  21.4s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  18.6s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  18.7s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.5min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  15.6s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  18.4s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  17.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  18.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  18.1s\n",
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      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  18.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  15.1s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  16.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  16.0s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.5min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  58.6s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  17.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  15.0s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  17.4s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  14.1s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  13.1s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  13.7s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  12.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  15.8s\n",
      "[CV] criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  14.1s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.6min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.5min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  17.8s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  21.1s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  20.8s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.5min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  19.5s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  22.7s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  19.6s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.6min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  19.4s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  24.1s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  21.3s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.4min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best, total=  57.7s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  18.3s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  18.3s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  14.5s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.5min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  18.4s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  14.2s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  18.0s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.6min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  17.9s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  15.4s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  18.7s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  13.7s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  19.6s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  15.8s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.3min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  16.9s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  17.3s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  14.6s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.2min\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  15.0s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  16.0s\n",
      "[CV] criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=gini, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  15.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.1min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  14.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  12.3s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  12.6s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  14.4s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  12.3s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  11.9s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=best, total=  57.4s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  14.5s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  13.5s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  12.9s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=best, total=  54.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  13.7s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  13.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  10.9s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=best, total=  53.3s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  13.8s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  12.7s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  13.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best, total=  57.2s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  13.6s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  11.8s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=4, min_samples_split=20, splitter=random, total=   9.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  57.7s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  59.8s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  54.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  14.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  12.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  12.4s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best, total=  59.6s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=best, total=  45.8s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  12.3s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  11.6s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  10.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best, total=  54.6s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=best, total=  52.0s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  14.1s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  12.6s\n",
      "[CV] criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=10, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  11.9s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.3min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.2min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  14.1s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  14.7s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  13.8s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.4min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=best, total=  54.8s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  15.9s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  14.8s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  14.8s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.4min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  15.8s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  12.8s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  12.9s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.3min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  15.4s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  12.0s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=5, splitter=random, total=  13.5s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.5min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.2min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.1min\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  17.8s\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  15.7s\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  15.3s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.2min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=best, total=  55.2s\n",
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      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  13.2s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  13.2s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  12.5s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  56.0s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  15.7s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  15.4s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  12.0s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.2min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  14.8s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  12.8s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  12.5s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.2min\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best, total=  59.1s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=best, total=  55.6s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  14.2s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  13.4s\n",
      "[CV] criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=20, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  11.9s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.3min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  17.1s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  14.3s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=5, splitter=random, total=  14.4s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.3min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=best, total= 1.1min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  15.7s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  14.8s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=10, splitter=random, total=  13.4s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.3min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  15.7s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=2, min_samples_split=20, splitter=random, total=  12.4s\n",
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      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.2min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best, total= 1.0min\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=5, splitter=best, total=  58.1s\n",
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      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.2min\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.1min\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  13.4s\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  15.4s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=10, splitter=random, total=  13.6s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.3min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=best, total= 1.0min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  13.1s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  13.7s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=4, min_samples_split=20, splitter=random, total=  12.5s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best, total= 1.3min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  59.4s\n",
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      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=best, total=  55.7s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  16.1s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  14.7s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=5, splitter=random, total=  13.6s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best, total= 1.3min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best, total=  58.4s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=best, total=  58.5s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  15.8s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  12.0s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=10, splitter=random, total=  10.5s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best, total= 1.3min\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best, total=  47.0s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=best, total=  52.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  13.3s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  12.4s\n",
      "[CV] criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=30, min_samples_leaf=6, min_samples_split=20, splitter=random, total=  11.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 324 out of 324 | elapsed: 227.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6425087108013937\n",
      "{'criterion': 'entropy', 'max_depth': 20, 'min_samples_leaf': 4, 'min_samples_split': 10, 'splitter': 'random'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source src=\"https://sound.peal.io/ps/audios/000/000/707/original/Good_Job!.wav\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtree1 = tree.DecisionTreeClassifier()\n",
    "# criterion=’gini’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1,\n",
    "dtparams = {'criterion':['gini', 'entropy'], 'splitter':['best','random'], 'max_depth':[30,40,50], 'min_samples_split':[10,20,30,40], 'min_samples_leaf':[4,6,8]}\n",
    "grid_dt = GridSearchCV(dtree1, dtparams, verbose=2)\n",
    "grid_dt.fit(x_train, y_train)\n",
    "grid_dt.predict(x_test)\n",
    "print(grid_dt.best_score_)\n",
    "print(grid_dt.best_params_)\n",
    "allDone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source src=\"https://sound.peal.io/ps/audios/000/000/707/original/Good_Job!.wav\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 39600 candidates, totalling 118800 fits\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.model_selection import RandomizedSearchCV\n",
    "rforest1 = RandomForestClassifier()\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 800, num = 100)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 10)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "\n",
    "# Create the random grid\n",
    "rfparams = {'criterion':['gini', 'entropy'],\n",
    "            'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "\n",
    "grid_rf = GridSearchCV(estimator = rforest1, param_grid= rfparams,  cv = 3, verbose=2,  n_jobs = -1)\n",
    "allDone()\n",
    "\n",
    "grid_rf.fit(x_train, y_train)\n",
    "allDone()\n",
    "print(grid_rf.grid_scores_)\n",
    "print(grid_rf.best_score_)\n",
    "print(grid_rf.best_params_)\n",
    "\n",
    "allDone()\n",
    "\n",
    "grid_rf.predict(x_test)\n",
    "gridacc=grid_rf.score(x_test,y_test)\n",
    "\n",
    "\n",
    "allDone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method BaseSearchCV.score of GridSearchCV(cv=3, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=2)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rf.score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without Grid-Seach ,With Bio-Inspired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UWaVv43_fj6r",
    "outputId": "4f74a579-a407-46f5-da84-45edd05a6d62"
   },
   "outputs": [
    {
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      "\n",
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     "output_type": "stream",
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      "0\n",
      "[0.784]\n",
      "[0.90133333]\n",
      "[0.864]\n",
      "[0.93866667]\n"
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    },
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      "\n",
      "  5%|████                                                                            | 1/20 [06:00<1:54:07, 360.38s/it]"
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      "1\n",
      "[0.76266667]\n",
      "[0.904]\n",
      "[0.864]\n",
      "[0.93866667]\n"
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      "\n",
      " 10%|████████▏                                                                         | 2/20 [06:14<56:09, 187.20s/it]"
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      "2\n",
      "[0.76266667]\n",
      "[0.89866667]\n",
      "[0.84266667]\n",
      "[0.93866667]\n"
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    },
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      "\n",
      " 15%|████████████▎                                                                     | 3/20 [06:29<36:49, 129.97s/it]"
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      "3\n",
      "[0.76266667]\n",
      "[0.89866667]\n",
      "[0.86933333]\n",
      "[0.93866667]\n"
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      "\n",
      " 20%|████████████████▍                                                                 | 4/20 [06:45<27:00, 101.28s/it]"
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    {
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     "output_type": "stream",
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      "4\n",
      "[0.76266667]\n",
      "[0.90666667]\n",
      "[0.85333333]\n",
      "[0.93866667]\n"
     ]
    },
    {
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      "\n",
      " 25%|████████████████████▊                                                              | 5/20 [06:59<20:58, 83.93s/it]"
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      "5\n",
      "[0.76266667]\n",
      "[0.896]\n",
      "[0.85333333]\n",
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    },
    {
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      "\n",
      " 30%|████████████████████████▉                                                          | 6/20 [07:14<16:53, 72.39s/it]"
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      "6\n",
      "[0.76266667]\n",
      "[0.896]\n",
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    },
    {
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      "\n",
      " 35%|█████████████████████████████                                                      | 7/20 [07:29<13:54, 64.16s/it]"
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      "7\n",
      "[0.76266667]\n",
      "[0.888]\n",
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    {
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      "8\n",
      "[0.76266667]\n",
      "[0.91466667]\n",
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    },
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      "[0.76266667]\n",
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      "[0.76266667]\n",
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     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'clf1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-88bb84b40115>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     38\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclfacc1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnacc1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrfacc1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtacc1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mclf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# the crow with maximum accuracy is printed\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'clf1' is not defined"
     ]
    }
   ],
   "source": [
    "# here the accuracy of each crows are calculated.\n",
    "neigh = KNeighborsClassifier()\n",
    "rforest =RandomForestClassifier()\n",
    "dtree = tree.DecisionTreeClassifier()\n",
    "clf = svm.SVC()\n",
    "\n",
    "nacc1=np.zeros((m,1),dtype='float64')\n",
    "rfacc1=np.zeros((m,1),dtype='float64')\n",
    "dtacc1=np.zeros((m,1),dtype='float64')\n",
    "clfacc1=np.zeros((m,1),dtype='float64')\n",
    "\n",
    "q=0\n",
    "for i in tqdm(range(m)):\n",
    "    print(i)\n",
    "    select=[]\n",
    "    for p in list(features):\n",
    "        if mem.loc[i,p]==1:\n",
    "            select.insert(q,p)\n",
    "            q=q+1\n",
    "    neigh.fit(x_train[select], y_train)\n",
    "    neigh.predict(x_test[select])\n",
    "    nacc1[i]=neigh.score(x_test[select],y_test) \n",
    "    print(nacc1[i])\n",
    "    \n",
    "    rforest.fit(x_train[select], y_train)\n",
    "    rforest.predict(x_test[select])\n",
    "    rfacc1[i]=rforest.score(x_test[select],y_test) \n",
    "    print(rfacc1[i])\n",
    "    \n",
    "    dtree.fit(x_train[select], y_train)\n",
    "    dtree.predict(x_test[select])\n",
    "    dtacc1[i]=dtree.score(x_test[select],y_test)\n",
    "    print(dtacc1[i])\n",
    "    \n",
    "    clf.fit(x_train[select], y_train)\n",
    "    clf.predict(x_test[select])\n",
    "    clfacc1[i]=clf.score(x_test[select],y_test)\n",
    "    print(clfacc1[i])\n",
    "\n",
    "print (nacc1.max(), rfacc1.max(), dtacc1.max(),clfacc1.max()) # the crow with maximum accuracy is printed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.784 0.9146666666666666 0.8693333333333333 0.9386666666666666\n"
     ]
    }
   ],
   "source": [
    "print (nacc1.max(), rfacc1.max(), dtacc1.max(),clfacc1.max()) # the crow with maximum accuracy is printed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NUMBER OF FEATURES SELECTED**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pAMS1eJkfj6_",
    "outputId": "f05d8b75-894c-480c-9657-c461161a125b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14368\n",
      "469\n",
      "469\n",
      "14368\n"
     ]
    }
   ],
   "source": [
    "print ((mem.loc[nacc1.argmax(),:]==1.0).sum())\n",
    "\n",
    "print ((mem.loc[rfacc1.argmax(),:]==1.0).sum())\n",
    "print ((mem.loc[dtacc1.argmax(),:]==1.0).sum())\n",
    "print ((mem.loc[clfacc1.argmax(),:]==1.0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No. of Iterations  VS Fitness Value**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wp1Gtqy1fj7p",
    "outputId": "d23b913e-5000-4872-dbf8-807683a8e2d4"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plotit' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-7dd202089067>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplotit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'No of iterations'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Fitness value'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plotit' is not defined"
     ]
    }
   ],
   "source": [
    "plotse=pd.Series(plotit)\n",
    "plt.plot(plotse)\n",
    "plt.xlabel('No of iterations')\n",
    "plt.ylabel('Fitness value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "mem.to_csv('memory.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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